Techniques for case allocation

ABSTRACT

Techniques for case allocation are disclosed. In one particular embodiment, the techniques may be realized as a method for case allocation comprising receiving, by at least one computer processor, at least one case allocation allocated using a first pairing strategy, and then reassigning, by the at least one computer processor, the at least one case allocation using behavioral pairing.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims priority to U.S. Provisional PatentApplication No. 62/261,780, filed Dec. 1, 2015, which is herebyincorporated by reference herein its entirety.

FIELD OF THE DISCLOSURE

This disclosure generally relates to customer service/contact centercase assignment, more particularly, to techniques for collaborative andnon-collaborative allocations of cases to agents using behavioralpairing.

BACKGROUND OF THE DISCLOSURE

In some customer service centers, cases may be assigned to agents (e.g.,analysts, specialists) for servicing. For example, insurance claims maybe assigned to insurance adjusters or other agents for subrogation orother processing; patients or other insureds may be assigned to nurses,pharmacists, or other clinical support specialists; debt collectors maybe assigned to debtor cases; and so on. These cases may be assigned in avariety of ways. In some customer service centers (including, forexample, workflow, case management, or transaction processing service orsupport organizations), cases may be assigned to agents based on time ofarrival. This strategy may be referred to as a “first-in, first-out”,“FIFO”, or “round-robin” strategy. In some customer service centers,management (e.g., managers or supervisors) may assign cases to agents(including other types of specialists such as those mentioned above),possibly with a particular rationale based on information known to themanagement, such as information about an agent's skills or historicalperformance. For some cases, management may have low confidence in theirassignments or lack relevant information to make optimal assignments.

Also, in some customer contact centers, cases or contacts may beassigned to agents for servicing. For example, a “lead list” of contactsmay be generated for each agent to contact (e.g., using an outbounddialer). These contacts may be assigned to agents using a FIFO strategy.In other environments, contacts may be assigned to agents using othermethods such as management-based assignments.

In view of the foregoing, it may be understood that there may besignificant problems and shortcomings associated with current FIFO ormanagement-assigned strategies.

SUMMARY OF THE DISCLOSURE

Techniques for case allocation are disclosed. In one particularembodiment, the techniques may be realized as a method for caseallocation comprising receiving, by at least one computer processor, atleast one case allocation allocated using a first pairing strategy, andthen reassigning, by the at least one computer processor, the at leastone case allocation using behavioral pairing.

In accordance with other aspects of this particular embodiment, thefirst pairing strategy is assigned by management.

In accordance with other aspects of this particular embodiment, thefirst pairing strategy is a first-in, first-out (FIFO) pairing strategy.

In accordance with other aspects of this particular embodiment, asubsequent reassignment of the at least one case allocation using thefirst pairing strategy may be received by the at least one computerprocessor.

In accordance with other aspects of this particular embodiment, asubsequent reversion of the at least one case allocation using the firstpairing strategy may be received by the at least one computer processor.

In accordance with other aspects of this particular embodiment, aplurality of case allocations allocated using the first pairing strategyis received by the at least one computer processor, the plurality ofcase allocations may be split by the at least one computer processorinto at least a first portion of cases and a second portion of cases,and the second portion of case allocations may be reassigned by the atleast one computer processor using behavioral pairing withoutreassigning the first portion of case allocations.

In accordance with other aspects of this particular embodiment, adifference in performance between the first portion of case allocationsand the second portion of case allocations may be determined by the atleast one computer processor.

In accordance with other aspects of this particular embodiment,splitting the plurality of cases is based in part on at least onerationale from management for at least one of the plurality of caseallocations.

In accordance with other aspects of this particular embodiment,splitting the plurality of cases is based in part on at least oneconfidence level from management for at least one of the plurality ofcase allocations.

In another particular embodiment, the techniques may be realized as asystem for case allocation comprising at least one computer processorconfigured to receive at least one case allocation allocated using afirst pairing strategy, and then reassign the at least one caseallocation using behavioral pairing. The system may also comprise atleast one memory, coupled to the at least one computer processor,configured to provide the at least one computer processor withinstructions.

In another particular embodiment, the techniques may be realized as anarticle of manufacture for case allocation comprising at least onenon-transitory computer processor readable medium and instructionsstored on the at least one medium, wherein the instructions areconfigured to be readable from the at least one medium by at least onecomputer processor and thereby cause the at least one computer processorto operate so as to receive at least one case allocation allocated usinga first pairing strategy and then reassign the at least one caseallocation using behavioral pairing.

The present disclosure will now be described in more detail withreference to particular embodiments thereof as shown in the accompanyingdrawings. While the present disclosure is described below with referenceto particular embodiments, it should be understood that the presentdisclosure is not limited thereto. Those of ordinary skill in the arthaving access to the teachings herein will recognize additionalimplementations, modifications, and embodiments, as well as other fieldsof use, which are within the scope of the present disclosure asdescribed herein, and with respect to which the present disclosure maybe of significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present disclosure,reference is now made to the accompanying drawings, in which likeelements are referenced with like numerals. These drawings should not beconstrued as limiting the present disclosure, but are intended to beillustrative only.

FIG. 1 shows a flow diagram of a collaborative allocation systemaccording to embodiments of the present disclosure.

FIG. 2 shows a flow diagram of a collaborative allocation methodaccording to embodiments of the present disclosure.

FIG. 3 shows a schematic representation of case splits according toembodiments of the present disclosure.

FIG. 4 shows a flow diagram of a non-collaborative allocation systemaccording to embodiments of the present disclosure.

FIG. 5 shows a flow diagram of a non-collaborative allocation methodaccording to embodiments of the present disclosure.

DETAILED DESCRIPTION

In some customer service centers, cases may be assigned to agents (e.g.,analysts, specialists) for servicing. For example, insurance claims maybe assigned to insurance adjusters or other agents for subrogation orother processing; patients or other insureds may be assigned to nurses,pharmacists, or other clinical support specialists; debt collectors maybe assigned to debtor cases; and so on. These cases may be assigned in avariety of ways. In some customer service centers (including, forexample, workflow, case management, or transaction processing service orsupport organizations), cases may be assigned to agents based on time ofarrival. This strategy may be referred to as a “first-in, first-out”,“FIFO”, or “round-robin” strategy. In some customer service centers,management (e.g., managers or supervisors) may assign cases to agents(including other types of specialists such as those mentioned above),possibly with a particular rationale based on information known to themanagement, such as information about an agent's skills or historicalperformance. For some cases, management may have low confidence in theirassignments or lack relevant information to make optimal assignments.

Also, in some customer contact centers, cases or contacts may beassigned to agents for servicing. For example, a “lead list” of contactsmay be generated for each agent to contact (e.g., using an outbounddialer). These contacts may be assigned to agents using a FIFO strategy.In other environments, contacts may be assigned to agents using othermethods such as management-based assignments.

In some embodiments, management assignments may be collaborativelyenhanced using an automated case assignment system, such as a behavioralpairing module as described in U.S. Ser. No. 14/871,658, filed Sep. 30,2015, now U.S. Pat. No. 9,300,802, issued Mar. 29, 2016, andincorporated by reference herein. In this way, a collaborativeallocation system may leverage a big data, artificial intelligencepairing solution (e.g., the behavioral pairing module) with managementexpertise (e.g., a management assignment module) to optimize caseassignment, resulting in increased performance in a customer servicecenter. For example, collaborative allocation or other uses ofbehavioral pairing of cases may result in increased subrogationrecoveries for insurance claims, improved care for medical patients,improved debt collection, and so on. In other embodiments, behavioralpairing and management-based pairing may be performed separately in anon-collaborative fashion.

In some embodiments, behavioral pairing may be performed “offline”(e.g., not in real time) to assign cases, generate lead lists, orperform other types of contact assignments using collaborative ornon-collaborative techniques.

Additionally, the improved performance of collaboratively-allocatedcases or non-collaboratively allocated cases as compared tomanagement-allocated cases may be precisely measurable as a gain (e.g.,1%, 3%, 5%, etc.). In some embodiments, gain may be precisely measuredusing a benchmarking module as described in U.S. patent application Ser.No. 15/131,915, filed Apr. 18, 2016.

FIG. 1 depicts the workflow of a collaborative allocation system 100according to some embodiments of the present disclosure.

Cases for assignment 110 may be received by, e.g., a managementassignment module 120 at a customer service center. The managementassignment module 120 may be provided solely by the customer servicecenter (including other types of customer service centers andaforementioned support organizations), or it may be provided in whole orin part as a component of a collaborative allocation system.

The management assignment module 120 may output initial assignment data130. Initial assignment data 130 may include pairings of cases withagents (including other types of agents and aforementioned specialists),and it may include management rationale for these pairings. For example,each pairing may have an associated score representing management'sconfidence (e.g., certainty) in a particular pairing. In someembodiments, each pairing may have one or more associated reason codesor other codes indicating management's reasons for a particular pairing(e.g., a good fit with agent's skills or personality given informationabout the agent known to management). Pairings may include an expectedlevel of time or effort (e.g., intensity) required to resolve the case.Pairings may also take into account balancing caseload across agentsincluding agents' capacities to take on additional cases with varyingrequirements for time or effort.

The initial assignment data 130 may be analyzed by a behavioral pairingmodule 140 (or similar pairing engine). At this point, some cases willbe excluded (i.e., reserved or frozen) by management. For example, ifmanagement has expressed high confidence or a particular reason code fora case, or if the behavioral pairing module 140 has determined lowability improve the initial assignment, the behavioral pairing module140 will not consider this case for reassignment.

The remaining cases may be split into cases that may be reassigned(e.g., an optimized or “on” group) and cases that may not be reassigned(e.g., a control or “off” group). This split may be done according toany of many possible splitting strategies. For example, management mayprovide a seed to a pseudorandom number generator, which may be used torandomly distributed cases into one group or the other. In someembodiments, cases will be divided evenly between the groups. In otherembodiments, an uneven distribution of cases may be used. For example,80% of the cases available for reassignment may be split into theoptimized group, while 20% of the cases available for reassignment maybe split into the control group. The technique used for splitting casesbetween the groups may be designed to ensure transparency and fairnesswhen benchmarking performance.

Following the splitting of the cases, the cases in the optimized groupmay be reassigned by the behavioral pairing module 140 or similarautomatic pairing techniques. In some embodiments, the behavioralpairing module 140 may incorporate data about the agents and themanagement (e.g., agent survey data 150A, management survey data 150B,historical data 150C). The survey may include self-assessment questions(e.g., which types of cases are you most skilled at? Which types ofcases do you prefer to handle? Which stage of a case are you mostskilled at? Which stage of a case do you prefer to handle?). Formanagement, survey questions may be directed at understanding amanager's rationale for assigning particular types of cases or cases atparticular stages to particular agents. Historical data may includeinformation such as historical case assignments and outcomes, case“scores” or other case assessments prior to assignment, and otherbaseline performance measurements. The behavioral pairing module 140 mayalso search/analyze/process other data sources for information that maybe relevant to optimizing assignments and creating artificialintelligence models. The behavioral pairing module 140 may account forany stage of the case management process to optimize case assignments,such as workflow, case management, transaction processing, etc.

The behavioral pairing module 140 may output reassignment data 160,which may include pairings from the optimized group that have beenreassigned to different agents. In some embodiments, the reassignmentdata 160 may be reviewed by the management assignment module 120, andthe management assignment module 120 may optionally output revisedreassignment data 170. For example, the revised reassignment data 170may optionally “undo”, revert, or otherwise change some of thereassigned pairings based, for example, on information known tomanagement.

Subsequently, the benchmarking module 180 may measure the gain inperformance attributable to the collaboration between management and thebehavioral pairing module 140. The benchmarking module 180 may processthe outcomes of each pairing to determine the relative performance ofcases in the optimized or “on” group, which were collaborativeallocated, against the performance of cases in the control or “off”group, which were allocated solely by management. The benchmarkingmodule 180 may output performance measurements 190 (e.g., gain) or otherinformation regarding the performance of the collaborative allocationsystem 100.

The collaborative allocation system 100 may repeat this process as newcases for assignment (e.g., cases for assignment 110) arrive orotherwise become ready to be allocated among the agents. In someembodiments, the management assignment module 120 or the behavioralpairing module may process results from earlier iterations to improvethe management process (e.g., train managers regarding certainrationales that were more or less effective than others) or thebehavioral pairing process (e.g., train or update the artificialintelligence algorithms or models).

In some embodiments, the collaborative allocation system 100 may operate“online” (e.g., in real time) as cases arrive at a queue or asmanagement assignments are made. In other embodiments, the collaborativeallocation system 100 may operate “offline” (e.g., not in real time), sothat a group of cases may be reassigned or otherwise allocated together.

FIG. 2 shows a flowchart of a collaborative allocation method 200according to embodiments of the present disclosure. At block 210,collaborative allocation method 200 may begin.

At block 210, preparatory information for collaborative allocation maybe processed. For example, an assignment or pairing module (e.g.,behavioral pairing module 140) may receive agent survey data, managementsurvey data, historical data, or other information for processing inpreparation for reassigning or otherwise allocating cases to agents.Collaborative allocation method 200 may proceed to block 220.

At block 220, initial assignment data (e.g., initial managementassignment data) may be received. In some embodiments, rationales formanagement assignments may also be received. Collaborative allocationmethod 200 may proceed to block 230.

At block 230, a portion of cases may be split out for reassignment,while another portion of cases may be excluded (reserved, frozen, orotherwise held back) from potential reassignment. In some embodiments,these cases may also be excluded from benchmarking measurements.Collaborative allocation method 200 may proceed to block 240.

At block 240, the portion of cases split out for reassignment may bereassigned. In some embodiments, reassignment may be performed by apairing module such as behavioral pairing module 140. In someembodiments, reassignment data may be output or otherwise returned formanagement review or further assignment. In some embodiments, a portionof the cases split out for reassignment may be designated to a controlgroup and will not be reassigned. Collaborative allocation method 200may proceed to block 250.

At block 250, revisions to reassignments, if any, may be received. Insome embodiments, management may revise, revert, or otherwise change thereassignments that were carried out by the pairing module at block 240.Revised or reverted cases may be included or excluded from benchmarkingmeasurements. Collaborative allocation method 200 may proceed to block260.

At block 260, the relative performance of collaboratively-assigned casesand management-assigned cases may be benchmarked or otherwise measured.In some embodiments, results from the comparison may be used to improvethe pairing module (e.g., artificial intelligence models of behavioralpairing module 140) or the rationales of management for subsequentmanagement assignments, or both.

Following block 260, collaborative allocation method 200 may end. Insome embodiments, collaborative allocation method 200 may return toblock 210 to begin allocating additional cases.

FIG. 3 depicts a schematic representation of case splits according toembodiments of the present disclosure. As shown in FIG. 3, seven agentsmay be assigned up to nine cases. Some cases may be designated as“Ongoing” (e.g., cases that were previously assigned but not yetcomplete). “Excluded” (i.e., frozen or held back) cases are casesassigned to an agent that were determined to not be made available forreassignment. “Management” cases are cases assigned to an agent thatwere made available for reassignment but were allocated to the controlgroup. “Joint” cases are cases allocated to the optimized group, whichwere jointly/collaboratively reassigned and/or revised by management.

In the example of FIG. 3, seven agents (labeled 1 to 7 in the “Agent”column) have a docket or queue of nine cases (labeled “Case 1” to “Case9” in the header row). Agent 1's first case (“Case 1”) is identified byan “O” for Ongoing, and cases 2-9 have been split for assignment orcollaborative allocation: Cases 3 and 8 (“E”) have been excluded fromcollaborative allocation, and may optionally be excluded from anybenchmarking or relative performance analysis. Cases 4, 5, and 7 (“M”)have been assigned by management, and may be benchmarked as being partof the control or off cycle. Cases 2, 6, and 9 (“J”) have been allocatedby an automated pairing strategy such as behavioral pairing, and may bebenchmarked as being part of the optimized or on cycle. In the case ofcollaborative allocation, the optimized pairings may be made jointlywith management. In other embodiments, such as non-collaborativeallocation, the optimized pairings may be made independently by thepairing strategy such as behavioral pairing, without revision orreassignment by management. The remaining agents Agent 2 to Agent 7 havebeen assigned or reassigned up to nine available cases in a similarmanner. As agents close cases in their dockets or queues, and as morecases become available for assignment, these new cases may be split forassignment or reassignment among the available agents according to thecollaborative or non-collaborative allocation techniques in use for thisset of agents.

The outcome of each case may be associated with whether a case wasongoing, excluded, management-assigned, or jointly-assigned using apairing strategy such as behavioral pairing. The relative performance ofdifferent assignment methodologies may be benchmarked or otherwisemeasured. For example, the performance gain attributable tojointly-assigned cases using behavioral pairing over management-assignedcases may be benchmarked.

FIG. 4 depicts the workflow of a non-collaborative allocation system 400according to some embodiments of the present disclosure.

Cases for assignment 110 may be received at a contact center. The casesmay be split into two or more groups for assignment by differentstrategies. In some embodiments, a portion of cases may be assignedrandomly, on a FIFO basis, by management, or other case allocationtechniques. A second portion of cases may be assigned using a pairingstrategy such as behavioral pairing. In some embodiments, as in theexample of FIG. 4, a first portion of cases may be received bymanagement assignment module 120, and a second portion of cases may bereceived by behavioral pairing module 140.

The management assignment module 120 may output management assignmentdata 410. Management assignment data 410 may include pairings of caseswith agents, and it may include management rationale for these pairings.For example, each pairing may have an associated score representingmanagement's confidence (e.g., certainty) in a particular pairing. Insome embodiments, each pairing may have one or more associated reasoncodes or other codes indicating management's reasons for a particularpairing (e.g., a good fit with agent's skills or personality giveninformation about the agent known to management). Pairings may includean expected level of time or effort (e.g., intensity) required toresolve the case. Pairings may also take into account balancing caseloadacross agents including agents' capacities to take on additional caseswith varying requirements for time or effort.

The behavioral pairing module 140 may output behavioral pairingassignment data 420. In some embodiments, the behavioral pairing module140 may incorporate data about the agents and the management (e.g.,agent survey data 150A, management survey data 150B, historical data150C). The survey may include self-assessment questions (e.g., whichtypes of cases are you most skilled at? Which types of cases do youprefer to handle? Which stage of a case are you most skilled at? Whichstage of a case do you prefer to handle?). For management, surveyquestions may be directed at understanding a manager's rationale forassigning particular types of cases or cases at particular stages toparticular agents. Historical data may include information such ashistorical case assignments and outcomes, case “scores” or other caseassessments prior to assignment, and other baseline performancemeasurements. The behavioral pairing module 140 may alsosearch/analyze/process other data sources for information that may berelevant to optimizing assignments and creating artificial intelligencemodels.

Subsequently, the benchmarking module 180 may measure the gain inperformance attributable to the behavioral pairing module 140 ascompared to the management assignment module (or other assignmentprocess such as a random or FIFO process). The benchmarking module 180may process the outcomes of each pairing to determine the relativeperformance of cases in the optimized group, which were allocated solelyusing behavioral pairing, against the performance of cases in thecontrol group, which were allocated solely by management. Thebenchmarking module 180 may output performance measurements 190 or otherinformation regarding the performance of the non-collaborativeallocation system 400.

The non-collaborative allocation system 400 may repeat this process asnew cases for assignment (e.g., cases for assignment 110) arrive orotherwise become ready to be allocated among the agents. In someembodiments, the management assignment module 120 or the behavioralpairing module 140 may process results from earlier iterations toimprove the management process (e.g., train managers regarding certainrationales that were more or less effective than others) or thebehavioral pairing process (e.g., train or update the artificialintelligence algorithms or models).

In some embodiments, the non-collaborative allocation system 400 mayoperate “online” (e.g., in real time) as cases arrive at a queue or asmanagement assignments are made. In other embodiments, thenon-collaborative allocation system 400 may operate “offline” (e.g., notin real time), so that a group of cases may be reassigned or otherwiseallocated together.

FIG. 5 shows a flow diagram of a non-collaborative allocation methodaccording to embodiments of the present disclosure. At block 510,non-collaborative allocation method 500 may begin.

At block 510, preparatory information for non-collaborative allocationmay be processed. For example, an assignment or pairing module (e.g.,behavioral pairing module 140) may receive agent survey data, managementsurvey data, historical data, or other information for processing inpreparation for assigning or otherwise allocating cases to agents.Non-collaborative allocation method 500 may proceed to block 520.

At block 520, cases may be split into first and second portions of oneor more cases. Non-collaborative allocation method 500 may proceed toblock 530.

At block 530, assignment data may be received for the portion of casessplit out for management assignment (or, e.g., random or FIFOassignment). Non-collaborative allocation method 500 may proceed toblock 540.

At block 540, the second portion of cases may be assigned using apairing strategy such as behavioral pairing (BP). Non-collaborativeallocation method 500 may proceed to block 550.

At block 550, the relative performance of BP-assigned cases andmanagement-assigned cases may be benchmarked or otherwise measured. Insome embodiments, results from the comparison may be used to improve thepairing module (e.g., artificial intelligence models of behavioralpairing module 140) or the rationales of management for subsequentmanagement assignments, or both.

Following block 550, non-collaborative allocation method 500 may end. Insome embodiments, non-collaborative allocation method 500 may return toblock 510 to begin allocating additional cases.

At this point it should be noted that collaborative andnon-collaborative allocation using behavioral pairing in accordance withthe present disclosure as described above may involve the processing ofinput data and the generation of output data to some extent. This inputdata processing and output data generation may be implemented inhardware or software. For example, specific electronic components may beemployed in a collaborative and non-collaborative allocation module,behavioral pairing module, benchmarking module, and/or similar orrelated circuitry for implementing the functions associated withcollaborative and non-collaborative allocation using behavioral pairing,such as in a workflow management system, contact center system, casemanagement system, etc. in accordance with the present disclosure asdescribed above. Alternatively, one or more processors operating inaccordance with instructions may implement the functions associated withcollaborative and non-collaborative allocation using behavioral pairingin accordance with the present disclosure as described above. If such isthe case, it is within the scope of the present disclosure that suchinstructions may be stored on one or more non-transitory computerprocessor readable storage media (e.g., a magnetic disk or other storagemedium), or transmitted to one or more computer processors via one ormore signals embodied in one or more carrier waves.

The present disclosure is not to be limited in scope by the specificembodiments described herein. Indeed, other various embodiments of andmodifications to the present disclosure, in addition to those describedherein, will be apparent to those of ordinary skill in the art from theforegoing description and accompanying drawings. Thus, such otherembodiments and modifications are intended to fall within the scope ofthe present disclosure. Further, although the present disclosure hasbeen described herein in the context of at least one particularimplementation in at least one particular environment for at least oneparticular purpose, those of ordinary skill in the art will recognizethat its usefulness is not limited thereto and that the presentdisclosure may be beneficially implemented in any number of environmentsfor any number of purposes. Accordingly, the claims set forth belowshould be construed in view of the full breadth and spirit of thepresent disclosure as described herein.

The invention claimed is:
 1. A method for allocating cases to agents ina service center system comprising: receiving, by at least one computerprocessor communicatively coupled to and configured to operate in theservice center system, at least one case allocation that was previouslyassigned using a first pairing strategy; selecting for reassignment, bythe at least one computer processor, the at least one case allocationusing a behavioral pairing strategy to optimize performance of theservice center system, wherein the behavioral pairing strategy isdifferent from the first pairing strategy, and wherein the optimizedperformance of the service center system is attributable to thebehavioral pairing strategy; and outputting, by the at least onecomputer processor, the selection of the at least one case allocationfor reassignment in the service center system.
 2. The method of claim 1,wherein the first pairing strategy is assigned by a managementauthority.
 3. The method of claim 1, wherein the first pairing strategyis a first-in, first-out (FIFO) pairing strategy.
 4. The method of claim1, further comprising receiving, by the at least one computer processor,a subsequent reassignment of the at least one case allocation using thefirst pairing strategy.
 5. The method of claim 1, further comprisingreceiving, by the at least one computer processor, a subsequentreversion of the at least one case allocation using the first pairingstrategy.
 6. The method of claim 1, further comprising: receiving, bythe at least one computer processor, a plurality of case allocationsthat were previously assigned using the first pairing strategy;splitting, by the at least one computer processor, the plurality of caseallocations into at least a first portion of cases and a second portionof cases; and reassigning, by the at least one computer processor, thesecond portion of case allocations using the behavioral pairing strategywithout reassigning the first portion of case allocations.
 7. The methodof claim 6, further comprising determining, by the at least one computerprocessor, a difference in performance between the first portion of caseallocations and the second portion of case allocations.
 8. The method ofclaim 6, wherein splitting the plurality of cases is based in part on atleast one rationale from a management authority for at least one of theplurality of case allocations.
 9. The method of claim 6, whereinsplitting the plurality of cases is based in part on at least oneconfidence level from a management authority for at least one of theplurality of case allocations.
 10. A system for allocating cases toagents in a service center system comprising: at least one computerprocessor communicatively coupled to and configured to operate in theservice center system, wherein the at least one computer processor isfurther configured to: receive at least one case allocation that waspreviously assigned using a first pairing strategy; select forreassignment the at least one case allocation using a behavioral pairingstrategy to optimize performance of the service center system, whereinthe behavioral pairing strategy is different from the first pairingstrategy, and wherein the optimized performance of the service centersystem is attributable to the behavioral pairing strategy; and outputthe selection of the at least one case allocation for reassignment inthe service center system; and at least one memory, coupled to the atleast one computer processor, configured to provide the at least onecomputer processor with instructions.
 11. The system of claim 10,wherein the first pairing strategy is assigned by a managementauthority.
 12. The system of claim 10, wherein the first pairingstrategy is a first-in, first-out (FIFO) pairing strategy.
 13. Thesystem of claim 10, wherein the at least one computer processor isfurther configured to receive a subsequent reassignment of the at leastone case allocation using the first pairing strategy.
 14. The system ofclaim 10, wherein the at least one computer processor is furtherconfigured to receive a subsequent reversion of the at least one caseallocation using the first pairing strategy.
 15. The system of claim 10,wherein the at least one computer processor is further configured to:receive a plurality of case allocations that were previously assignedusing the first pairing strategy; split the plurality of caseallocations into at least a first portion of cases and a second portionof cases; and reassign the second portion of case allocations using thebehavioral pairing strategy without reassigning the first portion ofcase allocations.
 16. The system of claim 15, wherein the at least onecomputer processor is further configured to determine a difference inperformance between the first portion of case allocations and the secondportion of case allocations.
 17. The system of claim 15, whereinsplitting the plurality of cases is based in part on at least onerationale from a management authority for at least one of the pluralityof case allocations.
 18. The system of claim 15, wherein splitting theplurality of cases is based in part on at least one confidence levelfrom a management authority for at least one of the plurality of caseallocations.
 19. An article of manufacture for allocating cases toagents in a service center system comprising: at least onenon-transitory computer processor readable medium; and instructionsstored on the at least one medium; wherein the instructions areconfigured to be readable from the at least one medium by at least onecomputer processor communicatively coupled to and configured to operatein the service center system and thereby cause the at least one computerprocessor to operate so as to: receive at least one case allocation thatwas previously assigned using a first pairing strategy; select forreassignment the at least one case allocation using a behavioral pairingstrategy to optimize performance of the service center system, whereinthe behavioral pairing strategy is different from the first pairingstrategy, and wherein the optimized performance of the service centersystem is attributable to the behavioral pairing strategy; and outputthe selection of the at least one case allocation for reassignment inthe service center system.
 20. The article of manufacture of claim 19,wherein the first pairing strategy is assigned by a managementauthority.
 21. The article of manufacture of claim 19, wherein the firstpairing strategy is a first-in, first-out (FIFO) pairing strategy. 22.The article of manufacture of claim 19, wherein the at least onecomputer processor is further caused to operate to receive a subsequentreassignment of the at least one case allocation using the first pairingstrategy.
 23. The article of manufacture of claim 19, wherein the atleast one computer processor is further caused to operate to receive asubsequent reversion of the at least one case allocation using the firstpairing strategy.
 24. The article of manufacture of claim 19, whereinthe at least one computer processor is further caused to operate to:receive a plurality of case allocations that were previously assignedusing the first pairing strategy; split the plurality of caseallocations into at least a first portion of cases and a second portionof cases; and reassign the second portion of case allocations using thebehavioral pairing strategy without reassigning the first portion ofcase allocations.
 25. The article of manufacture of claim 24, whereinthe at least one computer processor is further caused to operate todetermine a difference in performance between the first portion of caseallocations and the second portion of case allocations.
 26. The articleof manufacture of claim 24, wherein splitting the plurality of cases isbased in part on at least one rationale from a management authority forat least one of the plurality of case allocations.
 27. The article ofmanufacture of claim 24, wherein splitting the plurality of cases isbased in part on at least one confidence level from a managementauthority for at least one of the plurality of case allocations.